Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation
In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely on reinforcement learning to generate risky scenarios, making it difficult to efficiently learn realistic emergency behaviors. To address this issue, we propose a behavior guided method for generating high risk lane change scenarios. First, a behavior learning module based on an optimized sequence generative adversarial network is developed to learn emergency lane change behaviors from an extracted dataset. This design alleviates the limitations of existing datasets and improves learning from relatively few sam...
| Authors | Chen Xiong, Cheng Wang, Yuhang Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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